VIM
A simple implementation of "VIMA: General Robot Manipulation with Multimodal Prompts"
Appreciation
- Lucidrains
- Agorians
Install
pip install vima
Usage
import torch
from vima import Vima
# Generate a random input sequence
x = torch.randint(0, 256, (1, 1024)).cuda()
# Initialize VIMA model
model = Vima()
# Pass the input sequence through the model
output = model(x)
MultiModal Iteration
- Pass in text and and image tensors into vima
import torch
from vima.vima import VimaMultiModal
#usage
img = torch.randn(1, 3, 256, 256)
text = torch.randint(0, 20000, (1, 1024))
model = VimaMultiModal()
output = model(text, img)
License
MIT
Citations
@inproceedings{jiang2023vima,
title = {VIMA: General Robot Manipulation with Multimodal Prompts},
author = {Yunfan Jiang and Agrim Gupta and Zichen Zhang and Guanzhi Wang and Yongqiang Dou and Yanjun Chen and Li Fei-Fei and Anima Anandkumar and Yuke Zhu and Linxi Fan},
booktitle = {Fortieth International Conference on Machine Learning},
year = {2023}
}
1 maintainer
Metadata
1 maintainer
Release files for vima 0.0.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| vima-0.0.2.tar.gz | 25.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| vima-0.0.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 51.6 kB
Release files / vima-0.0.2.tar.gz
| Download URL | vima-0.0.2.tar.gz |
|---|---|
| Size | 25.3 kB |
| Tags | Source |
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Release files / vima-0.0.2-py3-none-any.whl
| Download URL | vima-0.0.2-py3-none-any.whl |
|---|---|
| Size | 26.3 kB |
| Tags | Python 3 |
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